Research
Original Investigation | META-ANALYSIS
Large-Scale Network Dysfunction in Major Depressive Disorder A Meta-analysis of Resting-State Functional Connectivity Roselinde H. Kaiser, PhD; Jessica R. Andrews-Hanna, PhD; Tor D. Wager, PhD; Diego A. Pizzagalli, PhD
IMPORTANCE Major depressive disorder (MDD) has been linked to imbalanced
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communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. OBJECTIVE To investigate network dysfunction in MDD through a meta-analysis of rsFC
studies. DATA SOURCES Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. STUDY SELECTION Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. DATA EXTRACTION AND SYNTHESIS Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. RESULTS Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. CONCLUSIONS AND RELEVANCE Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.
JAMA Psychiatry. doi:10.1001/jamapsychiatry.2015.0071 Published online March 18, 2015.
Author Affiliations: Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, Massachusetts (Kaiser, Pizzagalli); Department of Psychology and Neuroscience, University of Colorado, Boulder (Andrews-Hanna, Wager). Corresponding Author: Roselinde H. Kaiser, PhD, Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety, and Stress Research, McLean Hospital, 115 Mill St, Belmont, MA 02478 (RHKaiser @mclean.harvard.edu).
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Research Original Investigation
Meta-analysis of Network Dysfunction in Depression
M
ajor depressive disorder (MDD) is a psychiatric illness with devastating social, personal, and medical consequences.1,2 Moreover, MDD is ubiquitous, affecting more than 16 million people in the United States3 and 350 million people worldwide4 each year. Although significant progress has been made in understanding MDD and developing treatments, much is unknown about the pathophysiology of the disease, and rates of recurrence remain high.5 Exploring the neurobiological signature of MDD from new perspectives has the potential to transform current conceptualizations of the disease and sharpen the search for treatment targets.6 Researchers have become increasingly interested in the role of abnormal communication among large-scale functional brain networks in the pathophysiology of MDD. 6,7 Functional networks can be defined as distributed sets of brain regions that exhibit correlated activity at rest, that is, resting-state functional connectivity (rsFC), or during task performance.8,9 The recruitment of a highly synchronized network, in response to task demands or at rest, is believed to reflect distinct cognitive or emotional processes or mental states (eg, mind-wandering),10-12 although these relationships are complex and remain a rapidly evolving field of study. Of particular relevance are networks putatively related to processes affected in depression, such as the frontoparietal network (FN), involved in top-down regulation of attention and emotion; the default network (DN) and the dorsal attention network (DAN), involved in internally or externally oriented attention, respectively; and the affective network (AN) and the ventral attention network (VAN) (sometimes together c alled the salience network 1 3 ), involved in processing emotion or monitoring for salient events.14-16 For example, abnormal communication within the FN may underlie deficits in cognitive control, which are commonly observed in depression17 and may contribute to symptoms such as difficulty concentrating or regulating emotions. Likewise, aberrant communication between the FN and DN may reflect ongoing rumination or an underlying bias for control systems to allocate resources toward internal thoughts at the cost of engaging with the external world.18 Hence, specific patterns of network dysfunction may contribute to core deficits in cognitive and affective functioning that are believed to underlie clinical symptoms. Investigation of functional networks has surged in recent years, in particular in the domain of rsFC. Initial findings support the view that MDD is characterized by abnormal rsFC,19 but inconsistency in the location and nature of effects makes it difficult to unify this research. Variability across studies may emerge for several reasons, including small sample sizes or differences in the networks selected for study. For example, prior research using seed-based rsFC20, the most common analytic strategy, varies considerably in the location of seed regions of interest (ROIs). Although a spatially extensive set of seed ROIs provides a comprehensive view of rsFC across the brain, organizing results into a coherent model of network functioning is challenging. A theoretically informed strategy for categorizing seed ROIs and related findings (eg, by the location of E2
seed ROIs within functional networks) would help organize the diverse set of findings and allow for a direct test of replication across studies. Meta-analysis is arguably the most powerful tool for synthesizing this research because it is capable of evaluating whether effects are robust across differences in methodologic details and disentangling consistent effects from false-positive results. 2 1 , 2 2 However, although rsFC abnormalities related to MDD have been reviewed,19 meta-analysis of this burgeoning literature has, to our knowledge, never been performed. The present study aimed to fill this important gap by conducting a meta-analysis of seed-based rsFC studies and unifying findings in a neurocognitive model of depression. Primary analyses tested for consistency in the location of brain systems exhibiting depression-related hyperconnectivity or hypoconnectivity with seed ROIs, which in turn were categorized within a priori networks. On the basis of evidence for broad deficits in cognitive control in MDD,17 it was predicted that seed ROIs located within the FN would exhibit reduced connectivity with other areas of the FN. In addition, on the basis of the central role of ruminative, selfreferential thinking in cognitive models of depression,23,24 it was predicted that seed ROIs located within the DN would exhibit increased connectivity with other DN regions and increased connectivity with prefrontal regions of the FN involved in directing attention. Secondary analyses tested whether rsFC abnormalities were moderated by seed anatomy or by demographic or clinical factors.
Methods Literature Search A comprehensive literature search was conducted in Web of Science, PubMed, and EMBASE for articles in press as of June 30, 2014, using the keywords rest*(-ing), connect*(-ivity), and depress*(-ion, -ive). Manual searches were conducted within the reference sections of empirical and review articles and for publications that cited those articles. Original functional magnetic resonance imaging studies using whole-brain seedbased rsFC to compare individuals with MDD with a healthy control (HC) group were eligible for inclusion (other rsFC methods, such as independent components analysis, adopt a distinct statistical approach that cannot be aggregated with seedbased data). If a published study did not report whole-brain effects or did not provide seed ROI or peak effect coordinates, authors were contacted for this information. Exclusion criteria were as follows: (1) no HC group or no current MDD group; (2) non–seed-based method; (3) whole-brain results could not be retrieved or did not survive correction (metaanalyses of functional magnetic resonance imaging data test for consistency in the spatial location of significant effects across studies22; thus, only studies that reported group differences in rsFC were eligible for inclusion); (4) entirely overlapping sample and seed ROIs reported in another publication; or (5) seed ROI or peak effect coordinates could not be retrieved (eFigure in the Supplement). Publications reporting on the same sample but using different seed ROIs were coded as
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a single study; publications in which distinct MDD groups were each compared with a single HC group were coded as distinct studies, and supplementary analyses were conducted to address the issue of partial nonindependence.25 These searches and inclusion criteria yielded a sample of 27 studies from 25 publications26-50 that reported on 556 individuals with MDD and 518 healthy controls in the HC group (eTable 1 and eTable2 in the Supplement).
Data Extraction and Coding The present meta-analysis was coordinate based,21,22,51 with coordinates reflecting the locations of significant group differences in functional connectivity at the time series level. Data extraction and coding included the following. First, coordinates for the center of mass of each seed ROI (91 seeds) and the peak of each significant between-group effect (346 effects) were extracted for each study and converted to Montreal Neurological Institute space as needed.52 If the seed ROI was an anatomical region from a mask or standard brain atlas, the center of mass was calculated to obtain a representative coordinate. Second, each seed ROI was categorized into a seed–network based on the location of its center of mass within a priori rsFC networks defined by a previous whole-brain network parcellation in 1000 participants14-16 (eTable 3 in the Supplement). This network parcellation was selected given its full coverage of cortex, cerebellum, and striatum; its definition in a large sample; its replication across an independent sample; and its close correspondence with networks derived from alternative rsFC analytic strategies and task-based patterns of coactivation.53,54 Effects were also categorized based on the direction of effect (ie, hyperconnectivity or hypoconnectivity in MDD groups). In previous work, hyperconnectivity has been defined as larger positive or reduced negative rsFC in individuals with MDD compared with healthy controls; hypoconnectivity has been defined as larger negative or reduced positive rsFC in individuals with MDD compared with healthy controls. Because the distinction between enhanced and weakened connectivity was inconsistently reported in the studies reviewed, it was not possible to test these forms of rsFC abnormality separately. However, when reported in the original publication, patterns of abnormal rsFC related to stronger or weaker connectivity in individuals with MDD are noted in the Results.
Multilevel Kernel Density Analysis Meta-analysis22 was performed using the multilevel kernel density analysis toolbox (http://wagerlab.colorado.edu), a Matlab (MathWorks) toolbox that incorporates tools from Statistical Parametric Mapping (http://www.fil.ion.ucl.ac.uk /spm/). Coordinates for peak effects from each study and seed-network comparison were convolved with a spherical kernel (r = 15 mm55,56) and thresholded at a maximum value of 1, yielding an indicator map in which a value of 1 indicated a significant effect in the neighborhood and a value of 0 indicated no significant effect. Next, the density of effects across studies was computed by averaging the indicator maps, weighted by study sample size.21 The resulting denjamapsychiatry.com
Original Investigation Research
sity maps showed the proportion of studies in which hyperconnectivity or hypoconnectivity with each seed-network was observed in MDD within 15 mm of each voxel. Differences between density maps were calculated to test for directional effects (eg, either consistent hyperconnectivity or hypoconnectivity in MDD; unless otherwise noted, all effects were specific to one direction). A Monte Carlo simulation was performed to establish the familywise error rate threshold used to correct for multiple comparisons. In this simulation, the locations of significant effects from indicator maps were randomized within a gray-matter mask in 15 000 iterations, yielding an estimate of the maximum density of effects predicted to occur by chance. A familywise error rate threshold of P < .05 was met when the density statistic exceeded the maximum null in 95% of the Monte Carlo maps. Density maps can be thresholded based on height (density at that voxel exceeds the maximum expected over the entire brain by chance) or extent (density at multiple contiguous voxels exceeds the maximum expected in a cluster of that size by chance). Because these thresholds provide complementary information, both are reported. Findings are discussed in terms of within-network abnormalities (effects fall within the same functional network as seed ROIs) or between-network abnormalities (effects fall outside the functional network in which seed ROIs are located).
Post Hoc Analyses Three categories of post hoc tests were conducted. First, jackknife analyses were conducted to assess whether the inclusion of any partially nonindependent study disproportionately affected the results.25 To accomplish this, the density statistic for each significant cluster was iteratively recalculated leaving out each partially nonindependent study, and a χ2 test or Fisher exact test was performed between the original density statistic and the leave-one-out density statistic. Because these analyses failed to reveal disproportionate effects of any individual study, results reported here include all studies. Second, Fisher exact tests were conducted to investigate whether a specific anatomical region contributed more strongly to a significant effect than other regions of the same network. Although the primary analytic approach of grouping regions into functional networks made meta-analysis possible by boosting power across studies, this network-level approach made the assumption that distinct regions within each functional network show similar abnormalities in MDD. Therefore, post hoc region-level analyses were conducted by calculating the likelihood of a particular effect for seeds in distinct anatomical regions of a functional network and testing the difference in effect likelihood among regions. Third, analyses were performed to investigate moderation of effects by clinical and demographic factors (eTable 1 in the Supplement), including severity of depression (mild, moderate, or severe57-59), medication status (medication use or no use of medication in the MDD group), or age (teen, adult, or elder). For these analyses, the proportion of studies within each clinical or demographic group reporting the effect was calculated, and differences in proportions were tested between groups. (Reprinted) JAMA Psychiatry Published online March 18, 2015
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Table. Results of the Meta-analysis of Resting-State Functional Connectivity in Major Depressive Disordera Seed-Network (and Thresholding)
Effect Network
Effect Anatomy
MDD < HC (hb)
FN
Right PPC
MDD < HC (eb)
FN and DAN
Right PPC extending to SPL
MDD < HC (eb)
FN and DAN
Left PPC extending to SPL
MDD > HC (hb)
DN
Right hippocampus extending to MTG
MDD > HC (eb)
DN
MPFC
MDD > HC (hb)
FN
Left DLPFC
MDD > HC (eb)
FN
Left DLPFC
MDD > HC (eb)
FN
Left DLPFC
MDD < HC (eb)
VAN
FN
DN
AN
Seed Anatomy
y
z
Maximum P
44
−50
50
162
.54
46
−54
46
2074
.40
−42
−52
48
2285
.34
38
−30
−6
148
.30
−2
38
12
7456
.20
−42
26
32
90
.30
−38
26
36
784
.29
−34
34
36
1125
.18
Midcingulate extending to thalamus and putamen
−2
4
22
2476
.26
DN
MPFC
−2
46
16
3118
.37
FN, DN, visual network
Precuneus extending to PCC and occipital cortex
18
−66
34
6194
.32
ACC, amygdala, NACC
ACC, cerebellum, insula, putamen
MDD < HC (eb)
Abbreviations: ACC, anterior cingulate cortex; AN, affective network; DAN, dorsal attention network; DLPFC, dorsolateral prefrontal cortex; DN, default network; eb, extent based; FN, frontoparietal network; hb, height based; HC, healthy control; MDD, major depressive disorder; MPFC, medial prefrontal cortex; MTG, middle temporal gyrus; NACC, nucleus accumbens; VAN, ventral attention network.
Results Within-Network Abnormalities Hypoconnectivity Within the FN Major depressive disorder was associated with hypoconnectivity between the FN seeds and bilateral posterior parietal cortex (PPC), regions involved in attending to goal-relevant stimuli or features of an internal representation60 (Figure 1A and Table). Examining the original empirical studies revealed that, when reported, hypoconnectivity was related to weaker positive connectivity between the FN seeds and the PPC.26,27 Specifically, the FN seeds in the dorsolateral prefrontal cortex (DLPFC) or cerebellum exhibited hypoconnectivity with the PPC, and post hoc testing indicated that seeds in the DLPFC were more likely than cerebellar seeds to exhibit hypoconnectivity with right PPC (likelihood ratio, 5.29; P = .04), although no differences were detected for the left PPC (P = .53). Hypoconnectivity within the FN was not moderated by age, depression severity, or medication status (P > .05 for all; eTable 4 and eTable 5 in Supplement). Hyperconnectivity Within the DN Major depressive disorder was characterized by hyperconnectivity between the DN seeds and regions of the hippocampus extending to the middle temporal gyrus and areas of the medial prefrontal cortex (MPFC) (Figure 1B). These areas are beE4
Voxels
ACC, caudate, cerebellum, hippocampus, IPL, MPFC, MTG, PCC
MDD < HC (eb) VAN
x
Caudate, cerebellum, DLPFC
a
Coordinates are Montreal Neurological Institute standard stereotaxic spaces. Voxels indicate the number of 1 × 1 × 1-mm voxels. Maximum P is the maximum proportion of studies exhibiting the effect at the peak density weighted by sample size.
lieved to support internal mentation (eg, self-referential thinking and affective decision making).61 When reported in the original studies, within-DN hyperconnectivity was related to enhanced positive connectivity in MDD.27,30,32,34,45 Post hoc testing failed to reveal differences in the likelihood of hyperconnectivity as a function of seed anatomy (P > .05; eTable 4 and eTable 5 in Supplement). Neither age nor depression severity predicted DN hyperconnectivity (P > .05; eTable 4 and eTable 5 in Supplement), although trends emerged for greater likelihood of hyperconnectivity in unmedicated than medicated MDD between the DN seeds and the hippocampus (likelihood ratio, 6.01; P = .09) or the MPFC (likelihood ratio, 3.18; P = .12).
Between-Network Abnormalities Altered Connectivity Between the FN and Regions of the DAN or DN Involved in Externally or Internally Oriented Attention As reported above, MDD was associated with weaker rsFC between the FN seeds and regions of bilateral parietal cortex; these clusters extended to regions of the superior parietal lobule involved in attending to perceptual cues in the environment60 that fall within the DAN (Figure 1A). In addition, MDD was associated with hyperconnectivity between the DN seeds and a region of left DLPFC believed to be critical for goal-directed regulation of attention and emotion60,62-64 (Figure 1B). When reported, DN hyperconnectivity with lateral prefrontal regions was predominantly related to enhanced positive 3 2 , 3 4 , 4 5 but also weaker
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Figure 1. Meta-anaysis of Abnormal Resting-State Function Connectivity (rsFC) in Major Depressive Disorder (MDD) MDD>HC MDD .05; eTable 4 and eTable 5 in Supplement), and effects were not moderated by clinical or demographic variables (P > .05; eTable 4 and eTable 5 in Supplement). Altered Connectivity Between the AN and Regions of the DN Involved in Mediating Emotion Regulation Hypoconnectivity was observed between the AN seeds and regions of the MPFC involved in mediating emotion regulation65 (Figure 1C). When reported, hypoconnectivity was related to both weaker positive (between the nucleus accumbens and the MPFC35) and enhanced negative (between the amygdala and the MPFC46) connectivity. The likelihood of MPFC hypoconnectivity did not differ among anatomical regions of the AN (P > .05; eTable 4 in Supplement) and was not moderated by clinical or demographic variables (P > .05; eTable 5 in Supplement). Altered Connectivity Between the VAN and Regions of the FN or DN Major depressive disorder was linked to hypoconnectivity between the VAN seeds and regions of the precuneus extending to the occipital and posterior cingulate cortex (Figure 1D), a functionally diverse set of regions involved in visual attention and internal thought.60,61 There was no difference in likelihood of hyperconnectivity vs hypoconnectivity, suggesting generally abnormal connectivity between the VAN and posterior systems. Hypoconnectivity was also obser ved between the DN seeds and a region of the midcingulate extending to the thalamus and putamen (Figure 1B), areas involved in relaying information about salience and somatosensation. 13,16 When reported, such hypoconnectivity was related to weaker positive connectivity in MDD. 26,34 Post hoc analyses failed to reveal differences among anatomic al seeds in the likelihood of abnormal rsFC (P > .05; eTable 4 in Supplement) or moderation by clinical or demographic variables (P > .05; eTable 5 in Supplement).
Discussion The present study prov ides the first meta-analy tic evidence, to our knowledge, that individuals with MDD exhibit abnormal connectivity within and between brain networks involved in internally (DN) or externally (DAN) oriented attention, processing of emotion (AN) or salience (VAN), and goal-directed regulation of these functions (FN) (Figure 2). These findings motivate a neurocognitive model in which network dysfunction is tightly linked to deficits regulating attention and mood.6,7,23 In this model, reduced coordination among brain systems critical for cognitive control and altered communication between such control systems and other networks engaged for internal thought or emotional regulation may underlie the biased cognitive style and persistent negative mood that characterize MDD. E6
Reduced connectivity was observed in individuals with MDD among frontoparietal systems involved in cognitive control, and imbalanced connectivity was obser ved between control systems and regions engaged for externally directed attention or internal mentation. These findings converge with theoretical models in which depression is defined by the tendency to become mired in negative rumination,24 which in turn stems from abnormal communication among brain regions supporting goal-directed control of attention, emotion, and self-referential thought.23 A c o o rd i n at e - b a s e d s e a r c h o f p r i o r s t u d i e s (u s i ng BrainMap.org66) indicated that the same areas of the DLPFC that exhibited hypoconnectivity with external-attention systems and hyperconnectivity with internal-attention systems have been implicated in top-down control of cognitive functions.62-64 Critically, overlapping regions of the DLPFC exhibit abnormal activity in depressed individuals exerting cognitive control.67 Meanwhile, regions of the MPFC that were hyperconnected with other DN systems in the present meta-analysis have been implicated in functions such as self-referential thinking68 and autobiographical memory retrieval 69 and are hyperactive in depressed individuals instructed to direct attention away from self-focused thinking.70 Hence, the present patterns of poorly coordinated or imbalanced network functioning in MDD may reflect weaknesses in cognitive control that contribute to both general deficits in goal-directed behavior and specific biases toward internal thought at the cost of attending to the external world. The present meta-analysis also revealed hypoconnectivity in MDD between the MPFC and limbic regions. This pattern, considered in light of reduced connectivity among frontoparietal systems, suggests abnormal communication among networks involved in emotion regulation. Previous research has indicated that successful upregulation or downregulation of emotion relies on communication between lateral prefrontal cortex regions responsible for top-down control, areas of MPFC that mediate regulation, and limbic regions involved in affective responses.65,71 Altered activity and connectivity in this circuit have been observed in depressed individuals during emotion regulation tasks.72 Here, abnormal connectivity between regulatory and affective systems appeared to stem from both blunted positive communication (between the MPFC and nucleus accumbens) and excessive negative communication (between the MPFC and amygdala). Thus, hypoconnectivity between the MPFC and regions of the AN may stem from abnormalities in multiple subnetworks engaged for distinct facets of emotional processing. Although mixed, the present meta-analysis also provides evidence of hypoconnectivity between brain systems involved in processing salience and regions supporting cognitive control or internal mentation. The VAN is believed to play a role in signaling when to allocate resources to cognitive control systems in response to salient events or sensory experiences.73 Accordingly, decreased connectivity between the VAN and control systems could reflect reduced reorientation of attention in response to salient cues. However, the observed pattern of altered VAN connectivity included both hy-
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Figure 2. A Neurocognitive Network Model of Major Depressive Disorder (MDD)
FN
DN
DAN
VAN AN
poconnectivity and hyperconnectivity, suggesting that the nature of the VAN abnormality in MDD may depend on additional factors. For example, previous research revealed that, in response to negative emotional distractors, depression was associated with hyperconnectivity between regions responsive to salience and regions involved in internal mentation.18 Thus, the nature of communication between networks involved in salience and attention may be affected by the presence of environmental cues that correspond to the content of internal thoughts. Two general patterns emerged in this meta-analysis. First, the sources of abnormal connectivity within seednetworks tended to be spatially distributed, highlighting the importance of considering anatomical regions within functional networks. However, given the low frequency of any single seed ROI implemented across studies, the absence of anatomical specificity should be interpreted with caution. Second, network abnormalities were similar across demographic and clinical groups. However, these analyses could only compare differences in the likelihood (but not magnitude) of network abnormalities between clinical or demographic groups and only for groups that were consistently jamapsychiatry.com
Reduced connectivity among regions of the frontoparietal network (FN) may underlie general deficits in cognitive control, whereas increased connectivity between the FN and default network (DN) and reduced connectivity between the FN and dorsal attention network (DAN) may reflect biases toward ruminative thoughts at the cost of attending to the external world. Meanwhile, reduced connectivity between the affective network (AN) and medial prefrontal cortex regions that mediate top-down regulation may reflect impaired ability to upregulate or downregulate emotions or arousal, whereas abnormal connectivity between the ventral attention network (VAN) and posterior regions may reflect altered or biased salience monitoring. Black arrows indicate hypoconnectivity in MDD; white arrows, hyperconnectivity in MDD; and gray arrows, generally abnormal (both hypoconnectivity and hyperconnectivity in MDD).
identified across the original studies. Future studies investigating additional clinical constructs will provide a more nuanced view of rsFC in depression. Several limitations warrant attention and suggest directions for future research. First, the present meta-analysis was necessarily limited to seed-based rsFC studies and seed ROIs selected by those studies (eTable 3 in the Supplement). Hence, particular networks and anatomical regions were better represented than others. In addition, it was not possible to include findings from studies that adopted alternative analytic methods (eg, independent components analysis). Because relatively few prior studies have implemented these methods with MDD samples (eTable 2 in the Supplement), separate metaanalyses for each analytic approach could not be conducted. However, as this literature increases, an important next step will be to test the replicability of rsFC abnormalities across other analytic methods and network parcellations. Second, because rsFC is a rapidly evolving field, standards for data acquisition and processing varied considerably among the studies reviewed here (eTable 6 in the Supplement). Differences in motion correction or instructions to rest with eyes open vs closed may substantially (Reprinted) JAMA Psychiatry Published online March 18, 2015
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affect results.74 Unfortunately, it was not possible to test the moderating effects of such variables because of the low frequency of studies within methodologic categories, but these effects merit future investigation. Third, an important question unanswered by the present meta-analysis is the extent to which aberrant functional connectivity could be related to structural abnormalities.23 For example, decreased cortical thickness has been associated with altered functional connectivity in depressed adults.75 Future studies that integrate structural and functional perspectives may provide a more comprehensive view of neurobiological abnormalities in mood disorders. Fourth, it is unclear to what extent depression-related abnormalities in rsFC would persist during performance of other tasks. Resting-state functional connectivity appears to reflect both static (eg, related to anatomical connections) and dynamic (eg, related to changing goals or states of arousal) components, but the precise contribution of these components to rsFC is unknown.74 Abnormal rsFC in MDD may be a transient consequence of internally biased attention, related to ru-
ARTICLE INFORMATION Submitted for Publication: January 3, 2015; final revision received January 3, 2015; accepted January 20, 2015. Published Online: March 18, 2015. doi:10.1001/jamapsychiatry.2015.0071. Author Contributions: Drs Kaiser and Pizzagalli had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Kaiser, Wager, Pizzagalli. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Kaiser, Andrews-Hanna. Critical revision of the manuscript: All authors. Statistical analysis: Kaiser, Andrews-Hanna, Wager. Obtained funding: Kaiser, Pizzagalli. Administrative, technical, or material support: Andrews-Hanna, Pizzagalli. Study supervision: Wager, Pizzagalli. Conflict of Interest Disclosures: Dr Pizzagalli has reported receiving honoraria and consulting fees from Advanced Neuro Technology North America, AstraZeneca, Otsuka Pharmaceutical, Pfizer, and Servier for activities unrelated to this project. No other disclosures were reported. Funding/Support: This project was partially supported by grants R01 MH068376 and R01 MH101521 (Dr Pizzagalli), R01 MH101521 (Drs Wager and Andrews-Hanna), and R01 MH076136 (Drs Wager and Andrews-Hanna) from the National Institute of Mental Health and by The Phyllis and Jerome Lyle Rappaport Mental Health Research Fellowship (McLean Hospital) (Dr Kaiser).
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minating while resting in the scanner, rather than a persistent cause for biased or poorly controlled attention when engaged in other tasks. To disentangle these non–mutually exclusive possibilities, studies will be required that compare network functioning at rest and during tasks that challenge attention and mood regulation.
Conclusions To our knowledge, this study provides the first meta-analytic evidence of large-scale network dysfunction in MDD, including imbalanced connectivity among networks involved in regulating attention to the internal or external world and decreased connectivity among networks involved in regulating or responding to emotion or salience. These findings are consistent with a neurocognitive model of MDD in which abnormal communication among functional networks may mediate the core cognitive and affective biases that characterize this serious disorder.
database. No compensation was provided. We thank the authors of the included studies, with special thanks to authors who generously shared unpublished data from whole-brain analyses and seed ROI and peak coordinates for inclusion in this meta-analysis.
10. Buckner RL, Krienen FM. The evolution of distributed association networks in the human brain. Trends Cogn Sci. 2013;17(12):648-665.
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15. Choi EY, Yeo BTT, Buckner RL. The organization of the human striatum estimated by intrinsic functional connectivity. J Neurophysiol. 2012;108 (8):2242-2263.
5. Kessler RC, Petukhova M, Sampson NA, Zaslavsky AM, Wittchen HU. Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr Res. 2012;21(3):169-184.
16. Yeo BTT, Krienen FM, Sepulcre J, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125-1165.
6. Pizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology. 2011;36(1): 183-206. 7. Drevets WC, Price JL, Furey ML. Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct Funct. 2008;213(1-2):93-118.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.
8. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537-541.
Additional Contributions: Franziska Goer, BA, Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, Massachusetts, provided interrater checks on the
9. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100(1):253-258.
17. Snyder HR. Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. Psychol Bull. 2013;139(1):81-132. 18. Kaiser RH, Andrews-Hanna JR, Spielberg JM, et al. Distracted and down: neural mechanisms of affective interference in subclinical depression [published online July 25, 2014]. Soc Cogn Affect Neurosci. doi:10.1093/scan/nsu100. 19. Wang L, Hermens DF, Hickie IB, Lagopoulos J. A systematic review of resting-state functional-MRI studies in major depression. J Affect Disord. 2012; 142(1-3):6-12.
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20. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8(9): 700-711.
36. Gabbay V, Ely BA, Li Q, et al. Striatum-based circuitry of adolescent depression and anhedonia. J Am Acad Child Adolesc Psychiatry. 2013;52(6): 628-41.e13.
50. Ye T, Peng J, Nie B, et al. Altered functional connectivity of the dorsolateral prefrontal cortex in first-episode patients with major depressive disorder. Eur J Radiol. 2012;81(12):4035-4040.
21. Wager TD, Lindquist M, Kaplan L. Meta-analysis of functional neuroimaging data: current and future directions. Soc Cogn Affect Neurosci. 2007;2(2): 150-158.
37. Guo W, Liu F, Xue Z, et al. Abnormal resting-state cerebellar-cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression. Prog Neuropsychopharmacol Biol Psychiatry. 2013;44:5157.
51. Salimi-Khorshidi G, Smith SM, Keltner JR, Wager TD, Nichols TE. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage. 2009;45(3):810-823.
22. Wager TD, Lindquist MA, Nichols TE, Kober H, Van Snellenberg JX. Evaluating the consistency and specificity of neuroimaging data using meta-analysis. Neuroimage. 2009;45(1)(suppl): S210-S221. 23. Hasler G, Northoff G. Discovering imaging endophenotypes for major depression. Mol Psychiatry. 2011;16(6):604-619. 24. Holtzheimer PE, Mayberg HS. Stuck in a rut: rethinking depression and its treatment. Trends Neurosci. 2011;34(1):1-9. 25. Etkin A, Wager TD. Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. Am J Psychiatry. 2007;164(10):1476-1488. 26. Alalade E, Denny K, Potter G, Steffens D, Wang L. Altered cerebellar-cerebral functional connectivity in geriatric depression. PLoS One. 2011; 6(5):e20035. 27. Alexopoulos GS, Hoptman MJ, Kanellopoulos D, Murphy CF, Lim KO, Gunning FM. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disord. 2012;139(1):56-65. 28. Alexopoulos GS, Hoptman MJ, Yuen G, et al. Functional connectivity in apathy of late-life depression: a preliminary study. J Affect Disord. 2013;149(1-3):398-405. 29. Andreescu C, Tudorascu DL, Butters MA, et al. Resting state functional connectivity and treatment response in late-life depression. Psychiatry Res. 2013;214(3):313-321. 30. Berman MG, Peltier S, Nee DE, Kross E, Deldin PJ, Jonides J. Depression, rumination and the default network. Soc Cogn Affect Neurosci. 2011;6 (5):548-555. 31. Cao X, Liu Z, Xu C, et al. Disrupted resting-state functional connectivity of the hippocampus in medication-naïve patients with major depressive disorder. J Affect Disord. 2012;141(2-3):194-203. 32. Connolly CG, Wu J, Ho TC, et al. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biol Psychiatry. 2013;74(12):898-907.
38. Guo W, Liu F, Xue Z, et al. Decreased interhemispheric coordination in treatmentresistant depression: a resting-state fMRI study. PLoS One. 2013;8(8):e71368. 39. Guo W, Liu F, Dai Y, et al. Decreased interhemispheric resting-state functional connectivity in first-episode, drug-naive major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2013;41:24-29. 40. Guo W, Liu F, Liu J, et al. Is there a cerebellar compensatory effort in first-episode, treatment-naive major depressive disorder at rest? Prog Neuropsychopharmacol Biol Psychiatry. 2013; 46:13-18. 41. Hamilton JP, Chen G, Thomason ME, Schwartz ME, Gotlib IH. Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Mol Psychiatry. 2011;16(7):763-772. 42. Horn DI, Yu C, Steiner J, et al. Glutamaterigic and resting-state functional connectivity correlates of severity in major depression: the role of pregenual anterior cingulate cortex and anterior insula [published online July 15, 2010]. Front Syst Neurosci. doi:10.3389/fnsys.2010.00033. 43. Kenny ER, O’Brien JT, Cousins DA, et al. Functional connectivity in late-life depression using resting-state functional magnetic resonance imaging. Am J Geriatr Psychiatry. 2010;18(7):643651.
53. Smith SM, Fox PT, Miller KL, et al. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A. 2009;106(31):13040-13045. 54. Smith SM. The future of FMRI connectivity. Neuroimage. 2012;62(2):1257-1266. 55. Nee DE, Wager TD, Jonides J. Interference resolution: insights from a meta-analysis of neuroimaging tasks. Cogn Affect Behav Neurosci. 2007;7(1):1-17. 56. Thayer JF, Ahs F, Fredrikson M, Sollers JJ III, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev. 2012;36(2):747-756. 57. Beck AT, Steer RA, Ball R, Ranieri W. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess. 1996; 67(3):588-597. 58. Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382-389. 59. Zimmerman M, Martinez JH, Young D, Chelminski I, Dalrymple K. Severity classification on the Hamilton depression rating scale. J Affect Disord. 2013;150(2):384-388.
44. Lui S, Wu Q, Qiu L, et al. Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry. 2011;168(6):642-648.
60. Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol. 2008;100(6):3328-3342.
45. Ma C, Ding J, Li J, et al. Resting-state functional connectivity bias of middle temporal gyrus and caudate with altered gray matter volume in major depression. PLoS One. 2012;7(9):e45263.
61. Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron. 2010;65(4):550-562.
46. Pannekoek JN, van der Werff SJA, Meens PHF, et al. Aberrant resting-state functional connectivity in limbic and salience networks in treatment: naïve clinically depressed adolescents. J Child Psychol Psychiatry. 2014;55(12):1317-1327.
62. Fales CL, Barch DM, Rundle MM, et al. Altered emotional interference processing in affective and cognitive-control brain circuitry in major depression. Biol Psychiatry. 2008;63(4):377-384.
33. Cullen KR, Gee DG, Klimes-Dougan B, et al. A preliminary study of functional connectivity in comorbid adolescent depression. Neurosci Lett. 2009;460(3):227-231.
47. Sheline YI, Price JL, Yan Z, Mintun MA. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci U S A. 2010;107 (24):11020-11025.
34. Davey CG, Harrison BJ, Yucel M, Allen NB. Regionally specific alterations in functional connectivity of the anterior cingulate cortex in major depressive disorder. Psychol Med. 2012;42 (10):2071-2081.
48. Tahmasian M, Knight DC, Manoliu A, et al. Aberrant intrinsic connectivity of hippocampus and amygdala overlap in the fronto-insular and dorsomedial-prefrontal cortex in major depressive disorder. Front Hum Neurosci. 2013;7:639.
35. Furman DJ, Hamilton JP, Gotlib IH. Frontostriatal functional connectivity in major depressive disorder. Biol Mood Anxiety Disord. 2011; 1(1):11.
49. Tang Y, Kong L, Wu F, et al. Decreased functional connectivity between the amygdala and the left ventral prefrontal cortex in treatment-naive patients with major depressive disorder: a resting-state functional magnetic resonance imaging study. Psychol Med. 2013;43(9):1921-1927.
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52. Brett M, Christoff K, Cusack R, Lancaster J. Using the Talairach atlas with the MNI template. Neuroimage. 2001;13(6, pt 2):85-85.
63. Kerns JG. Anterior cingulate and prefrontal cortex activity in an FMRI study of trial-to-trial adjustments on the Simon task. Neuroimage. 2006; 33(1):399-405. 64. Veltman DJ, Rombouts SA, Dolan RJ. Maintenance versus manipulation in verbal working memory revisited: an fMRI study. Neuroimage. 2003;18(2):247-256. 65. Ochsner KN, Ray RD, Cooper JC, et al. For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. Neuroimage. 2004;23(2):483-499. 66. Laird AR, Lancaster JL, Fox PT. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics. 2005;3(1):65-78. 67. Dichter GS, Felder JN, Smoski MJ. Affective context interferes with cognitive control in unipolar
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depression: an fMRI investigation. J Affect Disord. 2009;114(1-3):131-142. 68. Gusnard DA, Akbudak E, Shulman GL, Raichle ME. Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(7): 4259-4264. 69. Kim H. A dual-subsystem model of the brain’s default network: self-referential processing, memory retrieval processes, and autobiographical memory retrieval. Neuroimage. 2012;61(4):966-977.
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70. Johnson MK, Nolen-Hoeksema S, Mitchell KJ, Levin Y. Medial cortex activity, self-reflection and depression. Soc Cogn Affect Neurosci. 2009;4(4): 313-327.
73. Corbetta M, Patel G, Shulman GL. The reorienting system of the human brain: from environment to theory of mind. Neuron. 2008;58 (3):306-324.
71. Wager TD, Davidson ML, Hughes BL, Lindquist MA, Ochsner KN. Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron. 2008;59(6):1037-1050.
74. Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci. 2013;16(7):832-837.
72. Johnstone T, van Reekum CM, Urry HL, Kalin NH, Davidson RJ. Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. J Neurosci. 2007;27(33):8877-8884.
75. van Tol MJ, Li M, Metzger CD, et al. Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychol Med. 2013;44(10):1-13.
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Original Investigation | META-ANALYSIS
Large-Scale Network Dysfunction in Major Depressive Disorder A Meta-analysis of Resting-State Functional Connectivity Roselinde H. Kaiser, PhD; Jessica R. Andrews-Hanna, PhD; Tor D. Wager, PhD; Diego A. Pizzagalli, PhD
IMPORTANCE Major depressive disorder (MDD) has been linked to imbalanced
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communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. OBJECTIVE To investigate network dysfunction in MDD through a meta-analysis of rsFC
studies. DATA SOURCES Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. STUDY SELECTION Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. DATA EXTRACTION AND SYNTHESIS Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. RESULTS Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. CONCLUSIONS AND RELEVANCE Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.
JAMA Psychiatry. doi:10.1001/jamapsychiatry.2015.0071 Published online March 18, 2015.
Author Affiliations: Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, Massachusetts (Kaiser, Pizzagalli); Department of Psychology and Neuroscience, University of Colorado, Boulder (Andrews-Hanna, Wager). Corresponding Author: Roselinde H. Kaiser, PhD, Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety, and Stress Research, McLean Hospital, 115 Mill St, Belmont, MA 02478 (RHKaiser @mclean.harvard.edu).
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Research Original Investigation
Meta-analysis of Network Dysfunction in Depression
M
ajor depressive disorder (MDD) is a psychiatric illness with devastating social, personal, and medical consequences.1,2 Moreover, MDD is ubiquitous, affecting more than 16 million people in the United States3 and 350 million people worldwide4 each year. Although significant progress has been made in understanding MDD and developing treatments, much is unknown about the pathophysiology of the disease, and rates of recurrence remain high.5 Exploring the neurobiological signature of MDD from new perspectives has the potential to transform current conceptualizations of the disease and sharpen the search for treatment targets.6 Researchers have become increasingly interested in the role of abnormal communication among large-scale functional brain networks in the pathophysiology of MDD. 6,7 Functional networks can be defined as distributed sets of brain regions that exhibit correlated activity at rest, that is, resting-state functional connectivity (rsFC), or during task performance.8,9 The recruitment of a highly synchronized network, in response to task demands or at rest, is believed to reflect distinct cognitive or emotional processes or mental states (eg, mind-wandering),10-12 although these relationships are complex and remain a rapidly evolving field of study. Of particular relevance are networks putatively related to processes affected in depression, such as the frontoparietal network (FN), involved in top-down regulation of attention and emotion; the default network (DN) and the dorsal attention network (DAN), involved in internally or externally oriented attention, respectively; and the affective network (AN) and the ventral attention network (VAN) (sometimes together c alled the salience network 1 3 ), involved in processing emotion or monitoring for salient events.14-16 For example, abnormal communication within the FN may underlie deficits in cognitive control, which are commonly observed in depression17 and may contribute to symptoms such as difficulty concentrating or regulating emotions. Likewise, aberrant communication between the FN and DN may reflect ongoing rumination or an underlying bias for control systems to allocate resources toward internal thoughts at the cost of engaging with the external world.18 Hence, specific patterns of network dysfunction may contribute to core deficits in cognitive and affective functioning that are believed to underlie clinical symptoms. Investigation of functional networks has surged in recent years, in particular in the domain of rsFC. Initial findings support the view that MDD is characterized by abnormal rsFC,19 but inconsistency in the location and nature of effects makes it difficult to unify this research. Variability across studies may emerge for several reasons, including small sample sizes or differences in the networks selected for study. For example, prior research using seed-based rsFC20, the most common analytic strategy, varies considerably in the location of seed regions of interest (ROIs). Although a spatially extensive set of seed ROIs provides a comprehensive view of rsFC across the brain, organizing results into a coherent model of network functioning is challenging. A theoretically informed strategy for categorizing seed ROIs and related findings (eg, by the location of E2
seed ROIs within functional networks) would help organize the diverse set of findings and allow for a direct test of replication across studies. Meta-analysis is arguably the most powerful tool for synthesizing this research because it is capable of evaluating whether effects are robust across differences in methodologic details and disentangling consistent effects from false-positive results. 2 1 , 2 2 However, although rsFC abnormalities related to MDD have been reviewed,19 meta-analysis of this burgeoning literature has, to our knowledge, never been performed. The present study aimed to fill this important gap by conducting a meta-analysis of seed-based rsFC studies and unifying findings in a neurocognitive model of depression. Primary analyses tested for consistency in the location of brain systems exhibiting depression-related hyperconnectivity or hypoconnectivity with seed ROIs, which in turn were categorized within a priori networks. On the basis of evidence for broad deficits in cognitive control in MDD,17 it was predicted that seed ROIs located within the FN would exhibit reduced connectivity with other areas of the FN. In addition, on the basis of the central role of ruminative, selfreferential thinking in cognitive models of depression,23,24 it was predicted that seed ROIs located within the DN would exhibit increased connectivity with other DN regions and increased connectivity with prefrontal regions of the FN involved in directing attention. Secondary analyses tested whether rsFC abnormalities were moderated by seed anatomy or by demographic or clinical factors.
Methods Literature Search A comprehensive literature search was conducted in Web of Science, PubMed, and EMBASE for articles in press as of June 30, 2014, using the keywords rest*(-ing), connect*(-ivity), and depress*(-ion, -ive). Manual searches were conducted within the reference sections of empirical and review articles and for publications that cited those articles. Original functional magnetic resonance imaging studies using whole-brain seedbased rsFC to compare individuals with MDD with a healthy control (HC) group were eligible for inclusion (other rsFC methods, such as independent components analysis, adopt a distinct statistical approach that cannot be aggregated with seedbased data). If a published study did not report whole-brain effects or did not provide seed ROI or peak effect coordinates, authors were contacted for this information. Exclusion criteria were as follows: (1) no HC group or no current MDD group; (2) non–seed-based method; (3) whole-brain results could not be retrieved or did not survive correction (metaanalyses of functional magnetic resonance imaging data test for consistency in the spatial location of significant effects across studies22; thus, only studies that reported group differences in rsFC were eligible for inclusion); (4) entirely overlapping sample and seed ROIs reported in another publication; or (5) seed ROI or peak effect coordinates could not be retrieved (eFigure in the Supplement). Publications reporting on the same sample but using different seed ROIs were coded as
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Meta-analysis of Network Dysfunction in Depression
a single study; publications in which distinct MDD groups were each compared with a single HC group were coded as distinct studies, and supplementary analyses were conducted to address the issue of partial nonindependence.25 These searches and inclusion criteria yielded a sample of 27 studies from 25 publications26-50 that reported on 556 individuals with MDD and 518 healthy controls in the HC group (eTable 1 and eTable2 in the Supplement).
Data Extraction and Coding The present meta-analysis was coordinate based,21,22,51 with coordinates reflecting the locations of significant group differences in functional connectivity at the time series level. Data extraction and coding included the following. First, coordinates for the center of mass of each seed ROI (91 seeds) and the peak of each significant between-group effect (346 effects) were extracted for each study and converted to Montreal Neurological Institute space as needed.52 If the seed ROI was an anatomical region from a mask or standard brain atlas, the center of mass was calculated to obtain a representative coordinate. Second, each seed ROI was categorized into a seed–network based on the location of its center of mass within a priori rsFC networks defined by a previous whole-brain network parcellation in 1000 participants14-16 (eTable 3 in the Supplement). This network parcellation was selected given its full coverage of cortex, cerebellum, and striatum; its definition in a large sample; its replication across an independent sample; and its close correspondence with networks derived from alternative rsFC analytic strategies and task-based patterns of coactivation.53,54 Effects were also categorized based on the direction of effect (ie, hyperconnectivity or hypoconnectivity in MDD groups). In previous work, hyperconnectivity has been defined as larger positive or reduced negative rsFC in individuals with MDD compared with healthy controls; hypoconnectivity has been defined as larger negative or reduced positive rsFC in individuals with MDD compared with healthy controls. Because the distinction between enhanced and weakened connectivity was inconsistently reported in the studies reviewed, it was not possible to test these forms of rsFC abnormality separately. However, when reported in the original publication, patterns of abnormal rsFC related to stronger or weaker connectivity in individuals with MDD are noted in the Results.
Multilevel Kernel Density Analysis Meta-analysis22 was performed using the multilevel kernel density analysis toolbox (http://wagerlab.colorado.edu), a Matlab (MathWorks) toolbox that incorporates tools from Statistical Parametric Mapping (http://www.fil.ion.ucl.ac.uk /spm/). Coordinates for peak effects from each study and seed-network comparison were convolved with a spherical kernel (r = 15 mm55,56) and thresholded at a maximum value of 1, yielding an indicator map in which a value of 1 indicated a significant effect in the neighborhood and a value of 0 indicated no significant effect. Next, the density of effects across studies was computed by averaging the indicator maps, weighted by study sample size.21 The resulting denjamapsychiatry.com
Original Investigation Research
sity maps showed the proportion of studies in which hyperconnectivity or hypoconnectivity with each seed-network was observed in MDD within 15 mm of each voxel. Differences between density maps were calculated to test for directional effects (eg, either consistent hyperconnectivity or hypoconnectivity in MDD; unless otherwise noted, all effects were specific to one direction). A Monte Carlo simulation was performed to establish the familywise error rate threshold used to correct for multiple comparisons. In this simulation, the locations of significant effects from indicator maps were randomized within a gray-matter mask in 15 000 iterations, yielding an estimate of the maximum density of effects predicted to occur by chance. A familywise error rate threshold of P < .05 was met when the density statistic exceeded the maximum null in 95% of the Monte Carlo maps. Density maps can be thresholded based on height (density at that voxel exceeds the maximum expected over the entire brain by chance) or extent (density at multiple contiguous voxels exceeds the maximum expected in a cluster of that size by chance). Because these thresholds provide complementary information, both are reported. Findings are discussed in terms of within-network abnormalities (effects fall within the same functional network as seed ROIs) or between-network abnormalities (effects fall outside the functional network in which seed ROIs are located).
Post Hoc Analyses Three categories of post hoc tests were conducted. First, jackknife analyses were conducted to assess whether the inclusion of any partially nonindependent study disproportionately affected the results.25 To accomplish this, the density statistic for each significant cluster was iteratively recalculated leaving out each partially nonindependent study, and a χ2 test or Fisher exact test was performed between the original density statistic and the leave-one-out density statistic. Because these analyses failed to reveal disproportionate effects of any individual study, results reported here include all studies. Second, Fisher exact tests were conducted to investigate whether a specific anatomical region contributed more strongly to a significant effect than other regions of the same network. Although the primary analytic approach of grouping regions into functional networks made meta-analysis possible by boosting power across studies, this network-level approach made the assumption that distinct regions within each functional network show similar abnormalities in MDD. Therefore, post hoc region-level analyses were conducted by calculating the likelihood of a particular effect for seeds in distinct anatomical regions of a functional network and testing the difference in effect likelihood among regions. Third, analyses were performed to investigate moderation of effects by clinical and demographic factors (eTable 1 in the Supplement), including severity of depression (mild, moderate, or severe57-59), medication status (medication use or no use of medication in the MDD group), or age (teen, adult, or elder). For these analyses, the proportion of studies within each clinical or demographic group reporting the effect was calculated, and differences in proportions were tested between groups. (Reprinted) JAMA Psychiatry Published online March 18, 2015
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Table. Results of the Meta-analysis of Resting-State Functional Connectivity in Major Depressive Disordera Seed-Network (and Thresholding)
Effect Network
Effect Anatomy
MDD < HC (hb)
FN
Right PPC
MDD < HC (eb)
FN and DAN
Right PPC extending to SPL
MDD < HC (eb)
FN and DAN
Left PPC extending to SPL
MDD > HC (hb)
DN
Right hippocampus extending to MTG
MDD > HC (eb)
DN
MPFC
MDD > HC (hb)
FN
Left DLPFC
MDD > HC (eb)
FN
Left DLPFC
MDD > HC (eb)
FN
Left DLPFC
MDD < HC (eb)
VAN
FN
DN
AN
Seed Anatomy
y
z
Maximum P
44
−50
50
162
.54
46
−54
46
2074
.40
−42
−52
48
2285
.34
38
−30
−6
148
.30
−2
38
12
7456
.20
−42
26
32
90
.30
−38
26
36
784
.29
−34
34
36
1125
.18
Midcingulate extending to thalamus and putamen
−2
4
22
2476
.26
DN
MPFC
−2
46
16
3118
.37
FN, DN, visual network
Precuneus extending to PCC and occipital cortex
18
−66
34
6194
.32
ACC, amygdala, NACC
ACC, cerebellum, insula, putamen
MDD < HC (eb)
Abbreviations: ACC, anterior cingulate cortex; AN, affective network; DAN, dorsal attention network; DLPFC, dorsolateral prefrontal cortex; DN, default network; eb, extent based; FN, frontoparietal network; hb, height based; HC, healthy control; MDD, major depressive disorder; MPFC, medial prefrontal cortex; MTG, middle temporal gyrus; NACC, nucleus accumbens; VAN, ventral attention network.
Results Within-Network Abnormalities Hypoconnectivity Within the FN Major depressive disorder was associated with hypoconnectivity between the FN seeds and bilateral posterior parietal cortex (PPC), regions involved in attending to goal-relevant stimuli or features of an internal representation60 (Figure 1A and Table). Examining the original empirical studies revealed that, when reported, hypoconnectivity was related to weaker positive connectivity between the FN seeds and the PPC.26,27 Specifically, the FN seeds in the dorsolateral prefrontal cortex (DLPFC) or cerebellum exhibited hypoconnectivity with the PPC, and post hoc testing indicated that seeds in the DLPFC were more likely than cerebellar seeds to exhibit hypoconnectivity with right PPC (likelihood ratio, 5.29; P = .04), although no differences were detected for the left PPC (P = .53). Hypoconnectivity within the FN was not moderated by age, depression severity, or medication status (P > .05 for all; eTable 4 and eTable 5 in Supplement). Hyperconnectivity Within the DN Major depressive disorder was characterized by hyperconnectivity between the DN seeds and regions of the hippocampus extending to the middle temporal gyrus and areas of the medial prefrontal cortex (MPFC) (Figure 1B). These areas are beE4
Voxels
ACC, caudate, cerebellum, hippocampus, IPL, MPFC, MTG, PCC
MDD < HC (eb) VAN
x
Caudate, cerebellum, DLPFC
a
Coordinates are Montreal Neurological Institute standard stereotaxic spaces. Voxels indicate the number of 1 × 1 × 1-mm voxels. Maximum P is the maximum proportion of studies exhibiting the effect at the peak density weighted by sample size.
lieved to support internal mentation (eg, self-referential thinking and affective decision making).61 When reported in the original studies, within-DN hyperconnectivity was related to enhanced positive connectivity in MDD.27,30,32,34,45 Post hoc testing failed to reveal differences in the likelihood of hyperconnectivity as a function of seed anatomy (P > .05; eTable 4 and eTable 5 in Supplement). Neither age nor depression severity predicted DN hyperconnectivity (P > .05; eTable 4 and eTable 5 in Supplement), although trends emerged for greater likelihood of hyperconnectivity in unmedicated than medicated MDD between the DN seeds and the hippocampus (likelihood ratio, 6.01; P = .09) or the MPFC (likelihood ratio, 3.18; P = .12).
Between-Network Abnormalities Altered Connectivity Between the FN and Regions of the DAN or DN Involved in Externally or Internally Oriented Attention As reported above, MDD was associated with weaker rsFC between the FN seeds and regions of bilateral parietal cortex; these clusters extended to regions of the superior parietal lobule involved in attending to perceptual cues in the environment60 that fall within the DAN (Figure 1A). In addition, MDD was associated with hyperconnectivity between the DN seeds and a region of left DLPFC believed to be critical for goal-directed regulation of attention and emotion60,62-64 (Figure 1B). When reported, DN hyperconnectivity with lateral prefrontal regions was predominantly related to enhanced positive 3 2 , 3 4 , 4 5 but also weaker
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Figure 1. Meta-anaysis of Abnormal Resting-State Function Connectivity (rsFC) in Major Depressive Disorder (MDD) MDD>HC MDD .05; eTable 4 and eTable 5 in Supplement), and effects were not moderated by clinical or demographic variables (P > .05; eTable 4 and eTable 5 in Supplement). Altered Connectivity Between the AN and Regions of the DN Involved in Mediating Emotion Regulation Hypoconnectivity was observed between the AN seeds and regions of the MPFC involved in mediating emotion regulation65 (Figure 1C). When reported, hypoconnectivity was related to both weaker positive (between the nucleus accumbens and the MPFC35) and enhanced negative (between the amygdala and the MPFC46) connectivity. The likelihood of MPFC hypoconnectivity did not differ among anatomical regions of the AN (P > .05; eTable 4 in Supplement) and was not moderated by clinical or demographic variables (P > .05; eTable 5 in Supplement). Altered Connectivity Between the VAN and Regions of the FN or DN Major depressive disorder was linked to hypoconnectivity between the VAN seeds and regions of the precuneus extending to the occipital and posterior cingulate cortex (Figure 1D), a functionally diverse set of regions involved in visual attention and internal thought.60,61 There was no difference in likelihood of hyperconnectivity vs hypoconnectivity, suggesting generally abnormal connectivity between the VAN and posterior systems. Hypoconnectivity was also obser ved between the DN seeds and a region of the midcingulate extending to the thalamus and putamen (Figure 1B), areas involved in relaying information about salience and somatosensation. 13,16 When reported, such hypoconnectivity was related to weaker positive connectivity in MDD. 26,34 Post hoc analyses failed to reveal differences among anatomic al seeds in the likelihood of abnormal rsFC (P > .05; eTable 4 in Supplement) or moderation by clinical or demographic variables (P > .05; eTable 5 in Supplement).
Discussion The present study prov ides the first meta-analy tic evidence, to our knowledge, that individuals with MDD exhibit abnormal connectivity within and between brain networks involved in internally (DN) or externally (DAN) oriented attention, processing of emotion (AN) or salience (VAN), and goal-directed regulation of these functions (FN) (Figure 2). These findings motivate a neurocognitive model in which network dysfunction is tightly linked to deficits regulating attention and mood.6,7,23 In this model, reduced coordination among brain systems critical for cognitive control and altered communication between such control systems and other networks engaged for internal thought or emotional regulation may underlie the biased cognitive style and persistent negative mood that characterize MDD. E6
Reduced connectivity was observed in individuals with MDD among frontoparietal systems involved in cognitive control, and imbalanced connectivity was obser ved between control systems and regions engaged for externally directed attention or internal mentation. These findings converge with theoretical models in which depression is defined by the tendency to become mired in negative rumination,24 which in turn stems from abnormal communication among brain regions supporting goal-directed control of attention, emotion, and self-referential thought.23 A c o o rd i n at e - b a s e d s e a r c h o f p r i o r s t u d i e s (u s i ng BrainMap.org66) indicated that the same areas of the DLPFC that exhibited hypoconnectivity with external-attention systems and hyperconnectivity with internal-attention systems have been implicated in top-down control of cognitive functions.62-64 Critically, overlapping regions of the DLPFC exhibit abnormal activity in depressed individuals exerting cognitive control.67 Meanwhile, regions of the MPFC that were hyperconnected with other DN systems in the present meta-analysis have been implicated in functions such as self-referential thinking68 and autobiographical memory retrieval 69 and are hyperactive in depressed individuals instructed to direct attention away from self-focused thinking.70 Hence, the present patterns of poorly coordinated or imbalanced network functioning in MDD may reflect weaknesses in cognitive control that contribute to both general deficits in goal-directed behavior and specific biases toward internal thought at the cost of attending to the external world. The present meta-analysis also revealed hypoconnectivity in MDD between the MPFC and limbic regions. This pattern, considered in light of reduced connectivity among frontoparietal systems, suggests abnormal communication among networks involved in emotion regulation. Previous research has indicated that successful upregulation or downregulation of emotion relies on communication between lateral prefrontal cortex regions responsible for top-down control, areas of MPFC that mediate regulation, and limbic regions involved in affective responses.65,71 Altered activity and connectivity in this circuit have been observed in depressed individuals during emotion regulation tasks.72 Here, abnormal connectivity between regulatory and affective systems appeared to stem from both blunted positive communication (between the MPFC and nucleus accumbens) and excessive negative communication (between the MPFC and amygdala). Thus, hypoconnectivity between the MPFC and regions of the AN may stem from abnormalities in multiple subnetworks engaged for distinct facets of emotional processing. Although mixed, the present meta-analysis also provides evidence of hypoconnectivity between brain systems involved in processing salience and regions supporting cognitive control or internal mentation. The VAN is believed to play a role in signaling when to allocate resources to cognitive control systems in response to salient events or sensory experiences.73 Accordingly, decreased connectivity between the VAN and control systems could reflect reduced reorientation of attention in response to salient cues. However, the observed pattern of altered VAN connectivity included both hy-
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Figure 2. A Neurocognitive Network Model of Major Depressive Disorder (MDD)
FN
DN
DAN
VAN AN
poconnectivity and hyperconnectivity, suggesting that the nature of the VAN abnormality in MDD may depend on additional factors. For example, previous research revealed that, in response to negative emotional distractors, depression was associated with hyperconnectivity between regions responsive to salience and regions involved in internal mentation.18 Thus, the nature of communication between networks involved in salience and attention may be affected by the presence of environmental cues that correspond to the content of internal thoughts. Two general patterns emerged in this meta-analysis. First, the sources of abnormal connectivity within seednetworks tended to be spatially distributed, highlighting the importance of considering anatomical regions within functional networks. However, given the low frequency of any single seed ROI implemented across studies, the absence of anatomical specificity should be interpreted with caution. Second, network abnormalities were similar across demographic and clinical groups. However, these analyses could only compare differences in the likelihood (but not magnitude) of network abnormalities between clinical or demographic groups and only for groups that were consistently jamapsychiatry.com
Reduced connectivity among regions of the frontoparietal network (FN) may underlie general deficits in cognitive control, whereas increased connectivity between the FN and default network (DN) and reduced connectivity between the FN and dorsal attention network (DAN) may reflect biases toward ruminative thoughts at the cost of attending to the external world. Meanwhile, reduced connectivity between the affective network (AN) and medial prefrontal cortex regions that mediate top-down regulation may reflect impaired ability to upregulate or downregulate emotions or arousal, whereas abnormal connectivity between the ventral attention network (VAN) and posterior regions may reflect altered or biased salience monitoring. Black arrows indicate hypoconnectivity in MDD; white arrows, hyperconnectivity in MDD; and gray arrows, generally abnormal (both hypoconnectivity and hyperconnectivity in MDD).
identified across the original studies. Future studies investigating additional clinical constructs will provide a more nuanced view of rsFC in depression. Several limitations warrant attention and suggest directions for future research. First, the present meta-analysis was necessarily limited to seed-based rsFC studies and seed ROIs selected by those studies (eTable 3 in the Supplement). Hence, particular networks and anatomical regions were better represented than others. In addition, it was not possible to include findings from studies that adopted alternative analytic methods (eg, independent components analysis). Because relatively few prior studies have implemented these methods with MDD samples (eTable 2 in the Supplement), separate metaanalyses for each analytic approach could not be conducted. However, as this literature increases, an important next step will be to test the replicability of rsFC abnormalities across other analytic methods and network parcellations. Second, because rsFC is a rapidly evolving field, standards for data acquisition and processing varied considerably among the studies reviewed here (eTable 6 in the Supplement). Differences in motion correction or instructions to rest with eyes open vs closed may substantially (Reprinted) JAMA Psychiatry Published online March 18, 2015
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affect results.74 Unfortunately, it was not possible to test the moderating effects of such variables because of the low frequency of studies within methodologic categories, but these effects merit future investigation. Third, an important question unanswered by the present meta-analysis is the extent to which aberrant functional connectivity could be related to structural abnormalities.23 For example, decreased cortical thickness has been associated with altered functional connectivity in depressed adults.75 Future studies that integrate structural and functional perspectives may provide a more comprehensive view of neurobiological abnormalities in mood disorders. Fourth, it is unclear to what extent depression-related abnormalities in rsFC would persist during performance of other tasks. Resting-state functional connectivity appears to reflect both static (eg, related to anatomical connections) and dynamic (eg, related to changing goals or states of arousal) components, but the precise contribution of these components to rsFC is unknown.74 Abnormal rsFC in MDD may be a transient consequence of internally biased attention, related to ru-
ARTICLE INFORMATION Submitted for Publication: January 3, 2015; final revision received January 3, 2015; accepted January 20, 2015. Published Online: March 18, 2015. doi:10.1001/jamapsychiatry.2015.0071. Author Contributions: Drs Kaiser and Pizzagalli had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Kaiser, Wager, Pizzagalli. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Kaiser, Andrews-Hanna. Critical revision of the manuscript: All authors. Statistical analysis: Kaiser, Andrews-Hanna, Wager. Obtained funding: Kaiser, Pizzagalli. Administrative, technical, or material support: Andrews-Hanna, Pizzagalli. Study supervision: Wager, Pizzagalli. Conflict of Interest Disclosures: Dr Pizzagalli has reported receiving honoraria and consulting fees from Advanced Neuro Technology North America, AstraZeneca, Otsuka Pharmaceutical, Pfizer, and Servier for activities unrelated to this project. No other disclosures were reported. Funding/Support: This project was partially supported by grants R01 MH068376 and R01 MH101521 (Dr Pizzagalli), R01 MH101521 (Drs Wager and Andrews-Hanna), and R01 MH076136 (Drs Wager and Andrews-Hanna) from the National Institute of Mental Health and by The Phyllis and Jerome Lyle Rappaport Mental Health Research Fellowship (McLean Hospital) (Dr Kaiser).
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minating while resting in the scanner, rather than a persistent cause for biased or poorly controlled attention when engaged in other tasks. To disentangle these non–mutually exclusive possibilities, studies will be required that compare network functioning at rest and during tasks that challenge attention and mood regulation.
Conclusions To our knowledge, this study provides the first meta-analytic evidence of large-scale network dysfunction in MDD, including imbalanced connectivity among networks involved in regulating attention to the internal or external world and decreased connectivity among networks involved in regulating or responding to emotion or salience. These findings are consistent with a neurocognitive model of MDD in which abnormal communication among functional networks may mediate the core cognitive and affective biases that characterize this serious disorder.
database. No compensation was provided. We thank the authors of the included studies, with special thanks to authors who generously shared unpublished data from whole-brain analyses and seed ROI and peak coordinates for inclusion in this meta-analysis.
10. Buckner RL, Krienen FM. The evolution of distributed association networks in the human brain. Trends Cogn Sci. 2013;17(12):648-665.
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